Exceeds

MARCH 2026

appgate.com Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the appgate.com engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The appgate.com engineering team reports 84.0% AI adoption, 1.53× productivity lift, and 19.8% code quality across recent work.

These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.

What this report measures

We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.

How to interpret these metrics

Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.

AI Adoption Rate

HIGH

84.0%

AI assistance is present in 84.0% of recent commits for appgate.com.

AI Productivity Lift

HIGH

1.53×

AI-enabled workflows deliver an estimated 53% lift in throughput.

AI Code Quality

LOW

19.8%

Review insights show 19.8% overall code health on AI-supported changes.

How is the appgate.com team performing with AI?

The appgate.com engineering team reports 84.0% AI adoption, translating into 1.53× productivity lift while sustaining 19.8% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.

Manager Questions Answered

Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.

What's a good company AI adoption rate?

appgate.com is at 84.0%. This is 40.3pp above the community median (43.7%)..

84.0%

Roughly in line43.7% Community Median

Spot squads sitting below the median and pair them with high-adoption champions to share workflows.

Does AI actually make developers faster?

appgate.com operates at 1.53×. This is 0.40× above the community median (1.13×)..

1.53×

↑0.40× above1.13× Community Median

Double down on automation around QA and release prep to compound the gains already in flight.

How does AI affect code quality?

appgate.com holds AI-assisted quality at 19.8%. This is 3.4pp below the community median (23.2%)..

19.8%

↓3.4pp below23.2% Community Median

Add structured AI code review rubrics and require human sign-off for critical surfaces.

How evenly is AI use distributed across our team?

AI impact is concentrated—92.4% of AI commits come from a few experts, raising enablement risk.

92.4%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

appgate.com has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.

Document case studies where AI accelerates delivery while maintaining quality, and expand playbooks across teams.

See how your full organization compares

Unlock personalized insights across all your repositories, teams, and contributors.

Securely connect Exceeds with your codebase to get commit-level insights on AI adoption and performance.

How Your Company Ranks

See how top engineering organizations compare across AI adoption, productivity lift, and code quality.

AI Adoption

% of commits with AI assistance

Companies in this quartile:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

IN

inngest.com

(4.82×)

U.

u.nus.edu

(2.87×)

AC

acad.pucrs.br

(1.12×)

MC

mcornholio.ru

(1.12×)

Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.

Code Quality

Post-merge defect rate

Companies in this quartile:

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

DR

draad.nl

(-82634.9%)

IN

inria.fr

(-2424.6%)

Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.

Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).

Top contributors

Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.

TM

Torger Miller

Commits41
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
RC

Raven Creighton

Commits48
AI Usage92.0%
Productivity Lift1.56x
Code Quality20.0%
MA

Masaya Ando

Commits15
AI Usage28.0%
Productivity Lift1.22x
Code Quality20.0%
DA

Dori Amara

Commits4
AI Usage20.0%
Productivity Lift1.11x
Code Quality20.0%
S'

Salvo 'LtWorf' Tomaselli

Commits36
AI Usage20.0%
Productivity Lift1.00x
Code Quality20.0%

Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.

Cross-Organization Network

Shared Repositories

1

RicardoRamirezG

appgate/sdpctl

ravenc-appgate

appgate/sdpctl

gokhan-berberoglu

appgate/sdpctl

Unknown contributor

No repositories listed

damara-appgate

appgate/sdpctl

hydrolucid

appgate/sdpctl

Activity

105 Commits

Your Network

10 People
hydrolucid
Member
damara-appgate
Member
RicardoRamirezG
Member
gokhan-berberoglu
Member
lars.kajes@appgate.com
Member
mandopaloooza
Member
per-allansson
Member
ravenc-appgate
Member
salvatore.tomaselli@appgate.com
Member

Why these metrics matter for engineering managers

Faster delivery

1.4x lift → predictable roadmaps

Safer velocity

93% quality → lower rollback risk

Equitable gains

AI less dependency on heroes

Governance

Depth monitoring audit-ready

ExceedsExceeds AI

Turns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.

See the truth of AI impact

Adoption + lift + quality in one view

Learn more

Know where to act first

Repo and role level "lift potential"

Learn more

Prove ROI

Export executive snapshots and benchmarks

Learn more